Virtual screening workflow development guided by the "receiver operating characteristic" curve approach. Application to high-throughput docking on metabotropic glutamate receptor subtype 4

Journal of Medicinal Chemistry

Volumn 48, Issue 7, 2005, Pages 2534-2547

  Triballeau, Nicolas ^a,b   Acher, Francine ^a   Brabet, Isabelle ^c   Pin, Jean Philippe ^c   Bertrand, Hugues Olivier ^b  

^a CNRS   (France)

^b Accelrys SARL   (France)

^c INSTITUT DE GÉNOMIQUE FONCTIONNELLE   (France)

Author keywords

[No Author keywords available]

Indexed keywords

1 AMINO 1,3 CYCLOPENTANEDICARBOXYLIC ACID; 2 AMINO 4 PHOSPHONOBUTYRIC ACID; CYCLOLEUCINE; GLUTAMIC ACID; METABOTROPIC RECEPTOR 4; METABOTROPIC RECEPTOR AGONIST; PHENYLGLYCINE;

ARTICLE; CELL STRAIN HEK293; DECISION MAKING; DRUG CONFORMATION; DRUG DESIGN; DRUG RESEARCH; HIGH THROUGHPUT SCREENING; HUMAN; HUMAN CELL; IN VITRO STUDY; RECEIVER OPERATING CHARACTERISTIC; SENSITIVITY AND SPECIFICITY; VIRTUAL REALITY;

BINDING SITES; DATABASES, FACTUAL; DRUG DESIGN; MODELS, MOLECULAR; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; RECEPTORS, METABOTROPIC GLUTAMATE; ROC CURVE;

EID: 17144385534     PISSN: 00222623     EISSN: None     Source Type: Journal    
DOI: 10.1021/jm049092j     Document Type: Article

References (70)

1. Neyman, J.; Pearson, E. S. On the problem of the most efficient tests of statistical hypotheses. Philos. Trans. R. Soc, London, Ser. A 1933, 231, 289-337.
2. Neyman, J.; Pearson, E. S. The testing of statistical hypotheses in relation to probabilities a priori. Proc. Cambridge Philos. Soc. 1933, 20, 492-510.
3. Green, D. M.; Swets, J. A. Signal Detection Theory and Psychophysics; John Wiley & Sons: New York, 1966.
4. Swets, J. A. The relative operating characteristic in psychology. Science 1973, 182, 990-1000.
5. Lusted, L. B. Decision making studies in patient management. N. Engl. J. Med. 1971, 284, 416-424.
6. Lusted, L. B. Signal detectability and medical decision-making. Science 1971, 171, 1217-1219.
7. Zweig, M. H.; Campbell, G. Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clin. Chem. 1993, 4, 561-577.
8. Torre, P.; Cruickshanks, K. J.; Nondahl, D. M.; Wiley, T. L. Distortion product otoacoustic emission response characteristics in older adults. Ear Hear. 2003, 24, 20-29.
9. Kharin, V. V.; Zwiers, F. W. On the ROC score of probability forecasts. J. Climate 2003, 16, 4145-4150.
10. Barbaree, H. E.; Seto, M. C.; Langton, C. M.; Peacock, E. J. Evaluating the predictive accuracy of six risk assessment instruments for adult sex offenders. Crim. Justice Behav. 2001, 28, 490-521.
11. Swets, J. A. Measuring the accuracy of diagnostic systems. Science 1988, 240, 1285-1293.
12. Swets, J. A.; Dawes, R. M.; Monahan, J. Better decisions through science. Sci. Am. 2000, 283, 82-87.
13. Pin, J.-P.; Acher, F. The metabotropic glutamate receptors: structure, activation mechanism and pharmacology. Curr. Drug Targets 2002, 1, 297-317.
14. Walters, W. P.; Stahl, M. T.; Murcko, M. A. Virtual screening-an overview. Drug Discovery Today 1998, 3, 160-178.
15. Böhm, H.-J.; Schneider, G. Virtual Screening for Bioactive Molecules; Wiley-VCH: Weinheim, Germany, 2000.
16. Bajorath, J. Integration of virtual screening and high-throughput screening. Nat. Rev. Drug Discovery 2002, 1, 882-894.
17. Sheridan, R. P.; Kearsley, S. K. Why do we need so many chemical similarity search methods? Drug Discovery Today 2002, 7, 903-911.
18. Willett, P. Chemical similarity searching. J. Chem. Inf. Comput. Sci. 1998, 38, 983-996.
19. Langer, T.; Hoffmann, R. D. Virtual screening: An effective tool for lead structure discovery? Curr. Pharm. Des. 2001, 7, 509-527.
20. Debnath, A. K. Quantitative structure-activity relationship (QSAR) paradigm-Hansch era to the new millennium. Mini-Rev. Med. Chem. 2001, 1, 187-195.
21. Wang, R.; Lu, Y.; Wang, S. Comparative evaluation of 11 scoring functions for molecular docking. J. Med. Chem. 2003, 46, 2287-2303.
22. Good, A. C.; Krystek, S. R.; Mason, J. S. High-throughput and virtual screening: core lead discovery technologies move towards integration. Drug Discovery Today 2000, 5, S61-S69.
23. Verdonk, M. L.; Berdini, V.; Hartshorn, M. J.; Mooij, W. T. M.; Murray, C. W.; et al. Virtual screening using protein-ligand docking: Avoiding artificial enrichment. J. Chem. Inf. Comput. Sci. 2004, 44, 793-806.
24. Manallack, D. T.; Pitt, W. R.; Gancia, E.; Montana, J. G.; Livingstone, D. J.; et al. Selecting screening candidates for kinase and G protein-coupled receptor targets using neural networks. J. Chem. Inf. Comput. Sci. 2002, 42, 1256-1262.
25. Manallack, D. T.; Tehan, B. G.; Gancia, E.; Hudson, B. D.; Ford, M. G.; et al. A consensus neural network-based technique for discriminating soluble and poorly soluble compounds. J. Chem. Inf. Comput. Sci. 2003, 43, 674-679.
26. Güner, O. F.; Henry, D. R. Metric for analyzing hit lists and pharmacophores. Pharmacophore Perception, Development and Use in Drug Design; International University Line: La Jolla CA, 2000; pp 193-212.
27. Bradley, E. K.; Miller, J. L.; Saiah, E.; Grootenhuis, P. D. J. Informative library design as an efficient strategy to identify and optimize leads: Application to cyclin-dependent kinase 2 antagonists. J. Med. Chem. 2003, 46, 4360-4364.
28. Jacobsson, M.; Lidén, P.; Stjernschantz, E.; Boström, H.; Norinder, U. Improving structure-based virtual screening by multivariate analysis of scoring data. J. Med. Chem. 2003, 46, 5781-5789.
29. Shepherd, A. J.; Gorse, D.; Thornton, J. M. Prediction of the location and type of β-turns in proteins using neural networks. Protein Sci. 1999, 8, 1045-1055.
30. Diller, D. J.; Li, R. Kinases, homology models, and high throughput docking. J. Med. Chem. 2003, 46, 4638-4647.
31. Matthews, B. Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochim. Biophys. Acta 1975, 405, 442-451.
32. Hubbard, R.; Bayarri, M. J. Confusion over measures of evidence (p's) versus errors (alpha's) in classical statistical testing. Am. Stat. 2003, 57, 171-178.
33. Bräuner-Osborne, H.; Egebjerg, J.; Nielsen, E. Ø.; Madsen, U.; Krogsgaard-Larsen, P. Ligands for Glutamate Receptors: Design and Therapeutic Prospects. J. Med. Chem. 2000, 43, 2609-2645.
34. Holden, C. Excited by glutamate. Science 2003, 300, 1866-1868.
35. Moldrich, R. X.; Chapman, A. G.; Sarro, G. D.; Meldrum, B. S. Glutamate metabotropic receptors as targets for drug therapy in epilepsy. Eur. J. Pharmacol. 2003, 476, 3-16.
36. Bhave, G.; Karim, F.; Carlton, S. M.; Gereau, R. W., IV. Peripheral group I metabotropic glutamate receptors modulate nociception in mice. Nat. Neurosci. 2001, 4, 417-423.
37. Neugebauer, V. Metabotropic glutamate receptors-important modulators of nociception and pain behavior. Pain 2002, 98, 1-8.
38. Kenny, P. J.; Markou, A. The ups and downs of addiction: role of metabotropic glutamate receptors. Trends Pharmacol. Sci. 2004, 25, 265-272.
39. Bergink, V.; Megen, H. J. G. M. v.; Westenberg, H. G. M. Glutamate and anxiety. Eur. Neuropsychopharmacol. 2004, 14, 175-183.
40. Marino, M. J.; Valenti, O.; Conn, P. J. Glutamate receptors and Parkinson's disease. Drugs Aging 2003, 20, 377-397.
41. Pin, J.-P.; Galvez, T.; Prézeau, L. Evolution, structure, and activation mechanism of family 3/C G-protein-coupled receptors. Pharmacol. Ther. 2003, 98, 325-354.
42. Kunishima, N.; Shimada, Y.; Tsuji, Y.; Sato, T.; Yamamoto, M.; et al. Structural basis of glutamate recognition by a dimeric metabotropic glutamate receptor. Nature 2000, 407, 971.
43. O'Hara, P. J.; Sheppard, P. O.; Thogersen, H.; Venezia, D.; Haldeman, B.; et al. The ligand-binding domain in metabotropic glutamate receptors is related to bacterial periplasmic binding proteins. Neuron 1993, 11, 41-52.
44. Bessis, A.-S.; Rondard, P.; Gaven, F.; Brabet, I.; Triballeau, N.; et al. Closure of the Venus Flytrap module of mGlu8 receptor and the activation process: insights from mutations converting antagonists into agonists. Proc. Natl. Acad. Sci. U.S.A. 2002, 99, 11097-11102.
45. Cartmell, J.; Schoepp, D. D. Regulation of neurotransmitter release by metabotropic glutamate receptors. J. Neurochem. 2000, 75, 889-907.
46. Schoepp, D. D.; Jane, D. E.; Monn, J. A. Pharmacological agents acting at subtypes of metabotropic glutamate receptors. Neuropharmacology 1999, 38, 1431-1476.
47. Teague, S. J. Implications of protein flexibility for drug discovery. Nat. Rev. Drug Discovery 2003, 2, 527-541.
48. Bertrand, H.-O.; Bessis, A.-S.; Pin, J.-P.; Acher, F. C. Common and selective molecular determinants involved in metabotopic glutamate receptor agonist activity. J. Med. Chem. 2002, 45, 3171-3183.
49. Hampson, D. R.; Huang, X.-P.; Pekhletski, R.; Peltekova, V.; Hornby, G.; et al. Probing the ligand-binding domain of the mGluR4 subtype of metabotropic glutamate receptor. J. Biol. Chem. 1999, 274, 33488-33495.
50. Venkatachalam, C. M.; Jiang, X.; Oldfield, T.; Waldman, M. LigandFit: a novel method for the shape-directed rapid docking of ligands to protein active sites. J. Mol. Graphics Modell. 2003, 21, 289-307.
51. Mayo, S. L.; Olafson, B. D.; Goddard, W. A. G., III. DREIDING: A generic force field for molecular simulation. J. Phys. Chem. 1990, 94, 8897-8909.
52. Gehlhaar, D. K.; Verkhivker, G. M.; Rejto, P. A.; Sherman, C. J.; Fogel, D. B.; et al. Molecular recognition of inhibitor AG-1343 by HIV-1 protease: conformationally flexible docking by evolutionary programming. Chem. Biol. 1995, 2, 317-324.
53. Mangoni, A.; Roccatano, D.; DiNola, A. Docking of flexible ligands to flexible receptors in solutions by molecular dynamics similation. Proteins 1999, 35, 153-162.
54. Keserû, G. M.; Kolossvary, I. Fully flexible low-mode docking: Application to induced fit in HIV integrase. J. Am. Chem. Soc. 2001, 123, 12708-12709.
55. Carlson, H. A. Protein flexibility and drug design: how to hit a moving target. Curr. Opin. Chem. Biol. 2002, 6, 447-452.
56. Blow, D. Outline of Crystallography for Biologists; Oxford University Press: New York, 2002; p 236.
57. Borman, S. Proteomics: taking off where genomics leaves off. Chem. Eng. News 2000, 78, 31-37.
58. Doman, T. N.; McGovern, S. L.; Witherbee, B. J.; Kasten, T. P.; Kurumbail, R.; et al. Molecular docking and high-throughput screening for novel inhibitors of protein tyrosine phosphatase-1B. J. Med. Chem. 2002, 45, 2213-2221.
59. Luu, P.; Acher, F.; Bertrand, H.-O.; Fan, J.; Ngai, J. Molecular determinants of ligand selectivity in a vertebrate odorant receptor. J. Neurosci., in press.
60. Speca, D. J.; Lin, D. M.; Sorensen, P. W.; Isakoff, E. Y.; Ngai, J.; et al. Functional identification of a goldfish odorant receptor. Neuron 1999, 23, 487-498.
61. Lipinski, C. A.; Lombardo, F.; Dominy, B. W.; Feeney, P. J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Delivery Rev. 1997, 23, 3-25.
62. Grüneberg, S.; Stubbs, M. T.; Klebe, G. Successful virtual screening for novel inhibitors of human carbonic anhydrase: strategy and experimental confirmation. J. Med. Chem. 2002, 45, 3588-3602.
63. Pritchard, J. F.; Jurima-Romet, M.; Reimer, M. L. J.; Mortimer, E.; Rolfe, B.; et al. Making better drugs: Decision gates in non-clinical drug development. Nat. Rev. Drug Discovery 2003, 2, 542-553.
64. Gehlhaar, D. K.; Bouzida, D.; Rejto, P. A. Rational Drug Design: Novel Methodology and Practical Applications; American Chemical Society: Washington, DC, 1999; pp 292-311.
65. Jain, A. N. Scoring noncovalent protein-ligand interactions: a continuous differentiable function tuned to compute binding affinities. J. Comput.-Aided Mol. Des. 1996, 10, 427-440.
66. Böhm, H.-J. The development of a simple empirical scoring function to estimate the binding constant for a protein-ligand complex of known three-dimensional structure. J. Comput.-Aided Mol. Des. 1994, 8, 243-256.
67. Muegge, I.; Martin, Y. C. A general and fast function for protein-ligand interactions: a simplified potential approach. J. Med. Chem. 1999, 42, 791-804.
68. Gomeza, J.; Mary, S.; Brabet, I.; Parmentier, M. L.; Restituito, S.; et al. Coupling of metabotropic glutamate receptors 2 and 4 to G alpha 15, G alpha 16, and chimeric G alpha q/i proteins: characterization of new antagonists. Mol. Pharmacol. 1996, 50, 923-930.
69. Goudet, C.; Gaven, F.; Kniazeff, J.; Vol, C.; Liu, J.; et al. Heptahelical domain of metabotropic glutamate receptor 5 behaves like rhodopsin-like receptors. Proc. Natl. Acad. Sci. U.S.A. 2004, 101, 378-383.
70. Ghose, A. K.; Viswanadhan, V. N.; Wendoloski, J. J. Prediction of Hydrophobic (lipophilic) properties of small organic molecules using fragmental methods: An analysis of ALOGP and CLOGP methods. J. Phys. Chem. A 1998, 102, 3762-3772.